Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Covers t-tests, p-values calculation, and comparison of coefficients.
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
